Other libraries:

functional zoo: PyTorch, unlike lua torch, has autograd in it's core, so using modular structure of torch.nn modules is not necessary, one can easily allocate needed Variables and write a function that utilizes them, which is sometimes more convenient. This repo contains model definitions in this functional way, with pretrained weights for some models.

torch-sampling: This package provides a set of transforms and data structures for sampling from in-memory or out-of-memory data.

torchcraft-py: Python wrapper for TorchCraft, a bridge between Torch and StarCraft for AI research.

pytorch-extension: This is a CUDA extension for PyTorch which computes the Hadamard product of two tensors.

tensorboard-pytorch: This module saves PyTorch tensors in tensorboard format for inspection. Currently supports scalar, image, audio, histogram features in tensorboard.

gpytorch: GPyTorch is a Gaussian Process library, implemented using PyTorch. It is designed for creating flexible and modular Gaussian Process models with ease, so that you don't have to be an expert to use GPs.

Tor10: A Generic Tensor-Network library that is designed for quantum simulation, base on the pytorch.

Catalyst: High-level utils for PyTorch DL & RL research. It was developed with a focus on reproducibility, fast experimentation and code/ideas reusing. Being able to research/develop something new, rather than write another regular train loop.

pytorch containers: This repository aims to help former Torchies more seamlessly transition to the "Containerless" world of PyTorch by providing a list of PyTorch implementations of Torch Table Layers.

Cnn-text classification: This is the implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in PyTorch.

deepspeech2: Implementation of DeepSpeech2 using Baidu Warp-CTC. Creates a network based on the DeepSpeech2 architecture, trained with the CTC activation function.

seq2seq: This repository contains implementations of Sequence to Sequence (Seq2Seq) models in PyTorch

Asynchronous Advantage Actor-Critic in PyTorch: This is PyTorch implementation of A3C as described in Asynchronous Methods for Deep Reinforcement Learning. Since PyTorch has a easy method to control shared memory within multiprocess, we can easily implement asynchronous method like A3C.

densenet: This is a PyTorch implementation of the DenseNet-BC architecture as described in the paper Densely Connected Convolutional Networks by G. Huang, Z. Liu, K. Weinberger, and L. van der Maaten. This implementation gets a CIFAR-10+ error rate of 4.77 with a 100-layer DenseNet-BC with a growth rate of 12. Their official implementation and links to many other third-party implementations are available in the liuzhuang13/DenseNet repo on GitHub.

nninit: Weight initialization schemes for PyTorch nn.Modules. This is a port of the popular nninit for Torch7 by @kaixhin.

faster rcnn: This is a PyTorch implementation of Faster RCNN. This project is mainly based on py-faster-rcnn and TFFRCNN.For details about R-CNN please refer to the paper Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks by Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun.

doomnet: PyTorch's version of Doom-net implementing some RL models in ViZDoom environment.

optnet: This repository is by Brandon Amos and J. Zico Kolter and contains the PyTorch source code to reproduce the experiments in our paper OptNet: Differentiable Optimization as a Layer in Neural Networks.

bandit-nmt: This is code repo for our EMNLP 2017 paper "Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback", which implements the A2C algorithm on top of a neural encoder-decoder model and benchmarks the combination under simulated noisy rewards.

yolo2-pytorch: The YOLOv2 is one of the most popular one-stage object detector. This project adopts PyTorch as the developing framework to increase productivity, and utilize ONNX to convert models into Caffe 2 to benifit engineering deployment.